Field of the Invention
The present invention relates to the determination of activity changes occurring in a system in response to stimuli applied thereto or a task undertaken thereby, and more particularly, to changes in brain activity as represented in electroencephalographic signals in response to stimuli provided thereto.
In electroencephalography, minute electrical signals produced in the brain are monitored, analyzed and often recorded. The interpretation of such signals forms a basis for neurological research and for neurological clinical diagnosis.
The electroencephalograph measures the electrical potential at the surface of the scalp of the subject's head by the use of electrodes pasted to the surface of the scalp at one or more of the standard positions adopted by the International Federation of Electroencephalography in what is called the 10/20 system. Typically, when used for a diagnosis, there may be as many as 20 electrodes provided in this manner which are connected to encephalographic equipment to provide indications of the potentials measured. These potentials are typically in the range of 1 to 100 .mu.v. These potentials can be spontaneous but are often measured in connection with the occurrance of some sort of brain stimulator controlled by the electroencephalographic equipment, such as a shifting light pattern to be perceived by the subject's eyes.
These electroencephalographic (EEG) signals, or measured potentials, have differing frequency content depending upon activities in the biological system including the brain. This frequency content has come to be classified into four basic frequency bands as follows: the "delta" band, 0 to less than 4 Hz; the "theta" band, 4 to less than 8 Hz; the "alpha" band, 8 to less than 13 Hz; and the "beta" band, greater than 13 Hz. A typical kind of information desired to be acquired from the EEG signals during a particular time period is the predominant frequency in a particular signal during that period. Determining this requires considerable training and is highly dependent on the skill of the neurologist, since an EEG signal portion typically includes many frequency components.
This analysis can be made more convenient and even improved by the use of signal processing equipment to provide parameters and characteristics of the data obtained in such EEG signals. For instance, providing one or more EEG signals to a computer properly programmed permits performing an analysis of the frequency spectrum contained in such signal or signals.
In such an arrangement, the EEG signal which is, of course, an analog signal, is sampled in amplitude over a selected interval of time with each such sample converted to its digital value and stored, at least temporarily, in the computer. These consecutive digitized samples, consecutive in the time order they are obtained from the sampled signal, are transformed from the time domain to the frequency domain by means of some fast Fourier transform (FFT) algorithm. The results of the transformation represent a frequency spectrum showing the frequency content in the signal sample measured. Such a spectrum can be displayed as a graph with the amplitude of the frequency components contained in the signal presented along a frequency axis.
Discrete Fourier transforms are defined for such digitized samples for which FFT algorithms can be used because such arrangements provide the corresponding frequency spectrum relatively quickly by reducing the amount of computation required. The samples could be acquired and ordinary Fourier methods used to obtain a frequency spectrum, but this would be a complex and time-consuming arrangement. Nevertheless, this latter arrangement has one virtue in that the interval over which the signal is acquired and sampled does not affect the outcome. In using the former arrangement to speed the conversion to the frequency domain, however, a limitation on the minimum amount of time in which data must be acquired is introduced because the length of that interval determines the period of the lowest frequency in the frequency spectrum resulting from use of such methods.
Because EEG signals contain very low frequencies in them which are of great interest to the neurologist, the intervals for acquiring data must be quite long if such frequencies are to be obtained. If shorter time intervals for obtaining data were used, the lowest frequency which could be contained in the spectrum would be a larger frequency value and information of interest would be lost. For example, a time interval of 1 second means that frequencies as low as about 1 Hz could be presented, but a data acquisition of 20 millisecond would result in the lowest frequency being presented being about 50 Hz.
This frequency domain representation problem is a significant problem because the changes in activity in a biological system in response to a stimulus will occur in time durations considerably less than one second, but frequency content in signals representing such changes will contain frequencies of interest in the range of 1 Hz. Thus, a desirable arrangement would show system activity changes occurring in reasonable fractions of a second after a stimulus including aspects of interest in such changes as contained in signals representing information with respect to these changes having frequency components of a period longer than the duration required for the changes to occur.